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One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

[Generated automatically as a Fitting summary]

Model Description

Name:d1cmp_cl_iov_05
Title:One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5
Author:PoPy for PK/PD
Abstract:
Population one Compartment Model with Absorption and Inter-occasion Variance
Here f[CL_isv] true value is 0.5
Keywords:one compartment model; dep_one_cmp_cl; iov
Input Script:d1cmp_cl_iov_05_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 0.5000 0.3309 0.1691 0.3383
f[CL] 1.0000 3.4613 2.4613 2.4613
f[V] 15.0000 19.7861 4.7861 0.3191

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE_STD] 0.2000 0.0921 0.1079 0.5393
f[ANOISE_STD] 0.2000 0.0507 0.1493 0.7464

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[CL_isv] 0.0100 0.5087 0.4987 49.8684
f[CL_iov] 0.0100 0.0070 0.0030 0.2955

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-414.4636

which required 1.21 iterations and took 372.36 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3309
f[CL] = 3.4613
f[V] = 19.7861
f[PNOISE_STD] = 0.0921
f[ANOISE_STD] = 0.0507
f[CL_isv] = 0.5087
f[CL_iov] = 0.0070

Fitted parameter .csv files

Fixed Effects:fx_params.csv (fit)
Random Effects:rx_params.csv (fit)
Model params:mx_params.csv (fit)
State values:sx_params.csv (fit)
Predictions:px_params.csv (fit)
Likelihoods:lx_params.csv (fit)

Inputs

Input Data:cx_obs_params.csv

Starting f[X] values (before fitting)

f[KA] = 0.5000
f[CL] = 1.0000
f[V] = 15.0000
f[PNOISE_STD] = 0.2000
f[ANOISE_STD] = 0.2000
f[CL_isv] = 0.0100
f[CL_iov] = 0.0100
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